224 research outputs found

    Coresets for Clustering with General Assignment Constraints

    Full text link
    Designing small-sized \emph{coresets}, which approximately preserve the costs of the solutions for large datasets, has been an important research direction for the past decade. We consider coreset construction for a variety of general constrained clustering problems. We significantly extend and generalize the results of a very recent paper (Braverman et al., FOCS'22), by demonstrating that the idea of hierarchical uniform sampling (Chen, SICOMP'09; Braverman et al., FOCS'22) can be applied to efficiently construct coresets for a very general class of constrained clustering problems with general assignment constraints, including capacity constraints on cluster centers, and assignment structure constraints for data points (modeled by a convex body B)\mathcal{B}). Our main theorem shows that a small-sized ϵ\epsilon-coreset exists as long as a complexity measure Lip(B)\mathsf{Lip}(\mathcal{B}) of the structure constraint, and the \emph{covering exponent} Λϵ(X)\Lambda_\epsilon(\mathcal{X}) for metric space (X,d)(\mathcal{X},d) are bounded. The complexity measure Lip(B)\mathsf{Lip}(\mathcal{B}) for convex body B\mathcal{B} is the Lipschitz constant of a certain transportation problem constrained in B\mathcal{B}, called \emph{optimal assignment transportation problem}. We prove nontrivial upper bounds of Lip(B)\mathsf{Lip}(\mathcal{B}) for various polytopes, including the general matroid basis polytopes, and laminar matroid polytopes (with better bound). As an application of our general theorem, we construct the first coreset for the fault-tolerant clustering problem (with or without capacity upper/lower bound) for the above metric spaces, in which the fault-tolerance requirement is captured by a uniform matroid basis polytope

    Three Puzzles on Mathematics, Computation, and Games

    Full text link
    In this lecture I will talk about three mathematical puzzles involving mathematics and computation that have preoccupied me over the years. The first puzzle is to understand the amazing success of the simplex algorithm for linear programming. The second puzzle is about errors made when votes are counted during elections. The third puzzle is: are quantum computers possible?Comment: ICM 2018 plenary lecture, Rio de Janeiro, 36 pages, 7 Figure

    Sapo: Reachability Computation and Parameter Synthesis of Polynomial Dynamical Systems

    Get PDF
    Sapo is a C++ tool for the formal analysis of polynomial dynamical systems. Its main features are: 1) Reachability computation, i.e., the calculation of the set of states reachable from a set of initial conditions, and 2) Parameter synthesis, i.e., the refinement of a set of parameters so that the system satisfies a given specification. Sapo can represent reachable sets as unions of boxes, parallelotopes, or parallelotope bundles (symbolic representation of polytopes). Sets of parameters are represented with polytopes while specifications are formalized as Signal Temporal Logic (STL) formulas

    Fault-tolerance in metric dimension of boron nanotubes lattices

    Get PDF
    The concept of resolving set and metric basis has been very successful because of multi-purpose applications both in computer and mathematical sciences. A system in which failure of any single unit, another chain of units not containing the faulty unit can replace the originally used chain is called a fault-tolerant self-stable system. Recent research studies reveal that the problem of finding metric dimension is NP-hard for general graphs and the problem of computing the exact values of fault-tolerant metric dimension seems to be even harder although some bounds can be computed rather easily. In this article, we compute closed formulas for the fault-tolerant metric dimension of lattices of two types of boron nanotubes, namely triangular and alpha boron. These lattices are formed by cutting the tubes vertically. We conclude that both tubes have constant fault tolerance metric dimension 4

    Notes on the Localization of Generalized Hexagonal Cellular Networks

    Get PDF
    The act of accessing the exact location, or position, of a node in a network is known as the localization of a network. In this methodology, the precise location of each node within a network can be made in the terms of certain chosen nodes in a subset. This subset is known as the locating set and its minimum cardinality is called the locating number of a network. The generalized hexagonal cellular network is a novel structure for the planning and analysis of a network. In this work, we considered conducting the localization of a generalized hexagonal cellular network. Moreover, we determined and proved the exact locating number for this network. Furthermore, in this technique, each node of a generalized hexagonal cellular network can be accessed uniquely. Lastly, we also discussed the generalized version of the locating set and locating number
    corecore